scholarly journals A collection of forcefield precursors for metal–organic frameworks

RSC Advances ◽  
2019 ◽  
Vol 9 (63) ◽  
pp. 36492-36507 ◽  
Author(s):  
Taoyi Chen ◽  
Thomas A. Manz
Keyword(s):  
Quantum Chemistry ◽  
Metal Organic Frameworks ◽  
Electron Cloud ◽  
Dispersion Coefficients ◽  
Cloud Parameters ◽  
Partial Charges ◽  
Quantum Chemistry Calculations ◽  
Metal Organic ◽  
Radial Moments

Atom-in-material (AIM) partial charges, dipoles and quadrupoles, dispersion coefficients (C6, C8, C10), polarizabilities, electron cloud parameters, radial moments, and atom types were extracted from quantum chemistry calculations for >3000 MOFs.

Message Passing Neural Networks for Partial Charge Assignment to Metal-Organic Frameworks

2020 ◽  
Author(s):  
Ali Raza ◽  
Arni Sturluson ◽  
Cory Simon ◽  
Xiaoli Fern
Keyword(s):  
Electronic Structure ◽  
Message Passing ◽  
Gas Adsorption ◽  
Metal Organic Frameworks ◽  
Computational Cost ◽  
Electronic Structure Calculations ◽  
High Fidelity ◽  
Partial Charges ◽  
Metal Organic ◽  
Structure Calculations

Virtual screenings can accelerate and reduce the cost of discovering metal-organic frameworks (MOFs) for their applications in gas storage, separation, and sensing. In molecular simulations of gas adsorption/diffusion in MOFs, the adsorbate-MOF electrostatic interaction is typically modeled by placing partial point charges on the atoms of the MOF. For the virtual screening of large libraries of MOFs, it is critical to develop computationally inexpensive methods to assign atomic partial charges to MOFs that accurately reproduce the electrostatic potential in their pores. Herein, we design and train a message passing neural network (MPNN) to predict the atomic partial charges on MOFs under a charge neutral constraint. A set of ca. 2,250 MOFs labeled with high-fidelity partial charges, derived from periodic electronic structure calculations, serves as training examples. In an end-to-end manner, from charge-labeled crystal graphs representing MOFs, our MPNN machine-learns features of the local bonding environments of the atoms and learns to predict partial atomic charges from these features. Our trained MPNN assigns high-fidelity partial point charges to MOFs with orders of magnitude lower computational cost than electronic structure calculations. To enhance the accuracy of virtual screenings of large libraries of MOFs for their adsorption-based applications, we make our trained MPNN model and MPNN-charge-assigned computation-ready, experimental MOF structures publicly available.<br>

Message Passing Neural Networks for Partial Charge Assignment to Metal-Organic Frameworks

2020 ◽  
Author(s):  
Ali Raza ◽  
Arni Sturluson ◽  
Cory Simon ◽  
Xiaoli Fern
Keyword(s):  
Electronic Structure ◽  
Message Passing ◽  
Gas Adsorption ◽  
Metal Organic Frameworks ◽  
Computational Cost ◽  
Electronic Structure Calculations ◽  
High Fidelity ◽  
Partial Charges ◽  
Metal Organic ◽  
Structure Calculations

Virtual screenings can accelerate and reduce the cost of discovering metal-organic frameworks (MOFs) for their applications in gas storage, separation, and sensing. In molecular simulations of gas adsorption/diffusion in MOFs, the adsorbate-MOF electrostatic interaction is typically modeled by placing partial point charges on the atoms of the MOF. For the virtual screening of large libraries of MOFs, it is critical to develop computationally inexpensive methods to assign atomic partial charges to MOFs that accurately reproduce the electrostatic potential in their pores. Herein, we design and train a message passing neural network (MPNN) to predict the atomic partial charges on MOFs under a charge neutral constraint. A set of ca. 2,250 MOFs labeled with high-fidelity partial charges, derived from periodic electronic structure calculations, serves as training examples. In an end-to-end manner, from charge-labeled crystal graphs representing MOFs, our MPNN machine-learns features of the local bonding environments of the atoms and learns to predict partial atomic charges from these features. Our trained MPNN assigns high-fidelity partial point charges to MOFs with orders of magnitude lower computational cost than electronic structure calculations. To enhance the accuracy of virtual screenings of large libraries of MOFs for their adsorption-based applications, we make our trained MPNN model and MPNN-charge-assigned computation-ready, experimental MOF structures publicly available.<br>

scholarly journals MOFs-Derived Zn-Based Catalysts in Acetylene Acetoxylation

Nanomaterials ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 98
Author(s):  
Mengli Li ◽  
Zhuang Xu ◽  
Yuhao Chen ◽  
Guowang Shen ◽  
Xugen Wang ◽  
...  
Keyword(s):  
Catalytic Activity ◽  
Photoelectron Spectroscopy ◽  
Active Sites ◽  
Metal Organic Frameworks ◽  
Catalytic Performance ◽  
Electron Cloud ◽  
Nitrogen Gas ◽  
X Ray ◽  
Metal Organic ◽  
Cloud Density

Metal–organic frameworks (MOFs)-derived materials with a large specific surface area and rich pore structures are favorable for catalytic performance. In this work, MOFs are successfully prepared. Through pyrolysis of MOFs under nitrogen gas, zinc-based catalysts with different active sites for acetylene acetoxylation are obtained. The influence of the oxygen atom, nitrogen atom, and coexistence of oxygen and nitrogen atoms on the structure and catalytic performance of MOFs-derived catalysts was investigated. According to the results, the catalysts with different catalytic activity are Zn-O-C (33%), Zn-O/N-C (27%), and Zn-N-C (12%). From the measurements of X-ray photoelectron spectroscopy (XPS), it can be confirmed that the formation of different active sites affects the electron cloud density of zinc. The electron cloud density of zinc affects the ability to attract CH3COOH, which makes catalysts different in terms of catalytic activity.

Metal-Organic Frameworks

2021 ◽  
Author(s):  
Lars Öhrström ◽  
Francoise M. Amombo Noa
Keyword(s):  
Metal Organic Frameworks ◽  
Metal Organic

Substrate templated synthesis of single-phase and uniform Zr-porphyrin-based metal–organic frameworks

2020 ◽  
Vol 7 (1) ◽  
pp. 221-231
Author(s):  
Seong Won Hong ◽  
Ju Won Paik ◽  
Dongju Seo ◽  
Jae-Min Oh ◽  
Young Kyu Jeong ◽  
...  
Keyword(s):  
Chemical Bath Deposition ◽  
Single Phase ◽  
Metal Organic Frameworks ◽  
Templated Synthesis ◽  
Pore Structures ◽  
Phase Pure ◽  
Metal Organic ◽  
Cbd Method

We successfully demonstrate that the chemical bath deposition (CBD) method is a versatile method for synthesizing phase-pure and uniform MOFs by controlling their nucleation stages and pore structures.

Sign in / Sign up

Export Citation Format

Share Document